In this paper, we provide a holistic overview of Conversational Machine Comprehension (CMC), which has seen a surge of research in recent years, owing to advancements in neural language modeling and the introduction of large-scale conversational datasets such as CoQA [1]} and QuAC [2]}. We discuss the challenges that make CMC different from machine reading comprehension (MRC) and compare the multi-turn conversational datasets: CoQA and QuAC, based on different CMC characteristics. To develop a high-level understanding of all the existing approaches to tackle CMC, we synthesize a general model framework and analyze the common trends across all the published models, loosely based on the components outlined in the framework. Finally, we discuss some open questions that emerged during our research and which, in our view, can be explored further. This review could serve as a compendium for researchers in this domain and help streamline research in CMC.
